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processes. This project focuses on designing and optimizing novel materials that enhance the efficiency and effectiveness of converting biomass into valuable products such as biofuels and fine chemicals. We
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infrastructure, has woven a sound academic and research network, and its recruitment process is seeking high-quality academics and professionals to boost its qualityoriented research environment in
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computer programming tools such as Matlab and/or Python. Knowledge of statistics and mathematical modeling. Experience in large spatial data processing, analysis, and interpretation. Experience in running
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of advanced pre-treatment techniques. Proficiency in kinetic and thermodynamic modeling of extraction processes. Expertise in using analytical tools (e.g., FTIR, XRD, SEM, TGA, TEM) for structural, thermal, and
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interpretation of remote sensing data, allowing for rapid decision-making in critical situations, such as during natural disasters. AI models can process big datasets efficiently, helping to make informed
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models, statistical analysis). Proficiency in programming languages (e.g., R, Python, MATLAB) and familiarity with relevant air quality modeling tools. Experience with large datasets and data processing
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Proficiency in using simulation software for process modeling (e.g., ASPEN, SuperPro Designer…). Familiarity with scaling up biotechnological processes and designing pilot-scale plants. Ability to analyze
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elemental quantification. Investigation of impurity phase removal (e.g., Fe, P, Si, REEs). Environmental and economic evaluation of processes via LCA and techno-economic models. Preparation of high-impact
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values across different omics layers and platforms. Cross-omics data fusion and representation learning for comprehensive systems biology modeling. Identification of causal relationships and biomarker
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city models focused on health and environmental infrastructures. Advanced Data Analysis: Advanced skills in machine learning, deep learning, and advanced statistics for processing complex data. Urban